error[template-injection]: code injection via template expansion
--> .github/workflows/jira_issue.yml:24:29
|
22 | run: |
| --- this run block
23 | # Escape special characters in title and body
24 | TITLE=$(echo '${{ github.event.issue.title }}' | sed 's/"/\\"/g' | sed "s/'/\\\'/g")
| ^^^^^^^^^^^^^^^^^^^^^^^^ may expand into attacker-controllable code
|
= note: audit confidence → High
= note: this finding has an auto-fix
> However, on issues events, github.event.pull_request is always null. So the condition reduces to (null != 'whitesource-for-github-com[bot]'). This is always true, and every GitHub user passes the gate.
Speaking broadly: it's a massive reminder that AI is trained on a veritable mountain of insecure GitHub Actions examples, many of which "fail open" in highly unpredictable ways even if widely used. Actions is almost unique in this regard, with the combination of a difficult-to-audit language and the type of privileged RCE environment that makes attackers salivate.
(I do think that this stems in part from GitHub's often-inscrutable documentation, and a decision to release Actions without a robust security linting solution, leaving that to the community - but I do understand how it's an uphill battle, and we could have ended up with a much less flexible CI/CD system without this having shipped fast.)
zizmor wouldn’t catch that condition at the moment, although it does have similar checks for other unsound conditions and incorrect/vulnerable bot actor checks. This one wouldn’t be too hard to add, though.
That would be awesome - and thanks for your work, I'll want to start using it!
Nullable event payloads silently null-coalescing to '' are a real "spooky action at a distance" kind of issue, because something that works perfectly when running and being QA'd on PRs, can silently fall apart if made to also run on the main/develop branch (which is only really monitored if the "build breaks," and thus a silent failure or skip might easily sneak through).
Our codebase indeed has comments like this, without which we'd be totally lost:
# Note contains('') is false if there is no PR at all e.g. on a push to develop,
# so this will always run on pushes to the develop branch.
if: ${{ !contains(github.event.pull_request.labels.*.name, 'ci-skip-backend') }}
Shell scripts on their own already are so perilous without static analysis. I'll never understand how we ended up deciding that embed them in yaml instead of requiring an external script file was a reasonable idea.
I get scared when I see these string interpolations in GitHub Actions.
Use `env:` instead and just work with environment variables in your shell script.
Yes, you still need to vet your script. Quoting is a common source of problems. Use shellcheck. Do not call eval/source/python/perl/whatever with untrusted input.
But you removed one layer of problems already by not pasting a value into your shell script code directly.
It can be that while also causing problems. It's like when a self driving car crashes, it's big news and everyone runs around like their pants are on fire. When a human driver crashes, it's a blip on local news or not even that.
Probably because there are 1000x or whatever human driven miles compared to AI. And no one wants to be crippled by an AI have have to fight $billon_dollar_corp for compensation.
The anti-hype is also way overblown. Nobody is saying that today's AI is omniscient and never makes mistakes. Future models will make fewer mistakes. They will still not hook into the universe with a cheat code and do everything at 100 percent reliability. It's a straw man.
Sadly I'm not. Either way, how LLMs work mean that traditional software analysis tools are every bit as important as they were in the before times. This is why we see a lot of hype around LLMs and formal verification.
But yes, there is an interesting change in the past decade, where everything new must be over-hyped.
Perhaps it is attention overload and needing to shout. Perhaps it’s that technological progress has significantly slowed while communication options have exploded (coincidence?).
I look at it a lot like EVs. They’re great, if your use case is inside the specific band. But, that isn’t who they were being marketed to. And now… “pushback” is putting it lightly.
Github Actions is actually so incredibly scary to have on public repo. It's full of so many footguns that's far from obvious.
It's a shame Github is buried under their current server issues, because it would be great to get improvements all of this - at least warning/erroring on these sorts of things themselves.
Would it be fair to say the blame falls squarely on Github? Why do they even allow pasting of arbitrary strings from a title directly into a script? And if they feel there is a reason, what did they imagine the safe way to do it was?
It's interesting to look at what was being attempted when the vulnerability was introduced[0]
> Workflows like jira_close.yml use deprecated atlassian JIRA actions and have a dependency on the gh-actions repo. This is not ideal and unecessarily complex. PR updates jira_close workflow to use direct API calls via curl. It preserves custom fields used too.
I won't speak to this projects' management and how they prioritize things, but from my own experience, pre-AI, this type of change would have been firmly in the "this is a minor annoyance, put it in the Tech Debt Backlog alongside the 50000 other tickets" and never actually done. The cost of a human investing the time understanding how to fix the problem, doing code changes, testing them, and deploying them is just way too high for what actual value this change brings, which is close to nothing.
Now with AI, it's as simple as firing up an agent and telling them to make a change; as much effort as writing that backlog Jira ticket in the first place.
Similar to the problem open source is having with low-value PRs, companies are going to have to start realizing that code is not free to review or maintain, even when it's generated for ~free, in their internal processes. Just because an agent can fix a minor tech debt annoyance with a few lines of instructions doesn't mean it should.
I have seen plenty of "my backlog has never been shorter" comments here.
I'm interested in how that turns out 6 months later.
In my team, we have plenty of enhancement requests from users. We address those that make obvious sense and are trivial to do but withhold from others, even though the code change itself is likely small. Because we don't know if there is more than a single user that can actually benefit from it, if it has unintended consequences, or if it causes maintainence issue down the road.
> I'm interested in how that turns out 6 months later.
That really is the real question, isn't it?
For me it has been a mixed bag so far, I've seen some companies use this tech in a slow and deliberate manner to do what they were already doing but a little bit faster. I've also seen hail Mary passes where the whole codebase was turned over to agents to go wild on with an undersized team and little to no QA. Time will tell...
> but withhold from others, even though the code change itself is likely small.
Prediction: programming is going to change massively not only because the cost of creating code will go down, but because people are so tired of this sort of gatekeeping "we know better" from programmers.
What you're describing as "gatekeeping" is actually Product Ownership and should be applauded, because the alternative is a product owner who abdicates responsibility to the customer.
Lots of responses to a comment I explicitly did not make. Let me try and spell it out for you folks:
demand was suppressed by an access bottleneck, the bottleneck is gone, expect a magnitude of change proportional to how much demand was actually pent up rather than proportional to how much programmers currently think was reasonable to deny.
Explicit things I am not saying: gatekeeping was bad, feature creep is good or desirable, etc. etc. etc.
Can you explain to all of us how this gatekeeping is actually bad? Software would be way, way worse if we just allowed any arbitrary feature to be added to software, and ordinary people who want all the things would have to learn, the hard way, that doing all the things is not actually good.
> Prediction: programming is going to change massively not only because the cost of creating code will go down, but because people are so tired of this sort of gatekeeping "we know better" from programmers.
I assume the "gatekeeping" decision to not implement a feature request is coming from someone responsible for the product, not from a developer.
Yeah, the YAMLification of everything kinda killed my ability to understand "everything". Previously, if you knew the Linux userland well, I felt like you could figure anything out with enough digging.
Take CI for example, it was Jenkins and it ran a csh/bash/zsh whatever script and captured the output. Nice and simple (even if the scripts sometimes got insane).
GitHub actions is nothing like that. Weird home grown extensions to YAML with their own idiosyncrasies and dynamically pulling in plugins from god knows where. You can't just take a workflow and execute it locally like you could with a bash script.
And worse: GitHub Actions not a full-fledged programming environment by itself either, so you're inevitably going to have to deal with nontrivial shell scripts on top of all the YAML mess.
Yeah. It makes sense when you're standing right next to it, but you take a step back and go "that doesn't look right". GitHub actions is the faster horse instead of a car.
The things done in yaml today would've been obscure bash oneliners had YAML not existed.
Jenkins still exists and it's no less complicated than Github Actions. The complexity gets hidden in obscure script files, obscure tabbed UI, and remote services for doing things Jenkins itself can't do. Defaulting to system tooling makes it almost impossible to predict what a job will do unless you know exactly how paths and tooling are set up (and what versions they're running).
None of my personal experiences with Jenkins had scripts that ran locally, they all relied on pre-installed software on the server because that was the thing people would do before the great YAMLification. You could copy-paste the Jenkins job, but unless you have Jabberwocky v2018.3 installed in /home/JabberWock/RELEASE, the script will fail.
The entire software development flow has been made incredibly complex by hooking up automations into every nook and cranny.
All of these complications need to be enabled manually, though. If your flow is complicated, you can cut it down to manageable size by doing a few more processes manually.
Luckily, all of the simplication and reproduction steps for Github also apply to Jenkins. Github YAML files are just scripts with different syntax, after all. Just like you can run bash locally, you can run act and reproduce whatever Github trigger you need. From there, you can simplify pipelines, stop curl2bashing "plugins", and so on.
Except that YAML in almost every devops or CI workflow is YAML plus obscure bash oneliners.
Or it's an obscure bash oneliner hidden in another repository as a package that you can depend on, which is either wrapped by another YAML config or maybe some JS.
I don't think the criticisms of Jenkins compare because Jenkins is really much more than a CI layer, predates the concept of devops and our modern understanding of CI/CD, and it has a lot of complexity to make that work. That was back when you'd have IT teams provisioning your servers for you and shit.
In a similar vein, JSON's lack of comments makes me marvel at how consistently JavaScript seems to choose the worse option. I'm oh so glad it found its way into config files
As a data/serialization format (the original intended use of JSON), I think no comments is the right choice, since adding comments introduces a bunch of questions about how comments ought to be parsed into a datastructure (or if they should be sent/(de)serialized at all). But for config files the lack of comments is the wrong choice, since typically you want to explain why things are set the way they are. The lack of ordering of entries also makes total sense for data/(de)serialization, but makes no sense for a config. I think the problem is people are lazy and just want to JSON.parse() everything, and used JSON in places it doesn't belong, like config files.
Not the person you’re replying to but you were clear in what you were describing.
It seems like they were thinking of some json parsers that’ll ignore just straight up anything after // before a new line in JSON (looking at you visual studio). So a .NET dev who has never touched anything but Visual Studio may make comments in the appsettings.json for a .NET project and it made all my command line parsers throw errors. Thanks Microsoft.
I never figured out how the hell to write YAML and I definitely won't now that I trust the AI to do a better job than me. It's so unintuitive.
Every time I've tried in the past, something as simple as making a value a list had some nonsense expectations. I can't wrap my head around how that spec got any traction and wasn't laughed off the face of the earth the first time it was looked at by someone who didn't create it.
When I see YAML in a product tech stack, I know that the developers have probably made other similarly poor life decisions and try to steer clear of the entire iceberg.
We're going to see more of this before we see, hopefully, substantially less of it.
What I'm seeing now in industry -- and I think this autofix issue is a precise example of it -- is a natural evolution of the "LGTM!" review that's so prevalent in software development and similar disciplines.
For years, the dramatic majority of "code review" was a quick glance followed by "Looks good to me." Sure, critical workflows have more scrutiny. Sure, not everyone fell victim to this trap. Sure, there are many exceptions. But it's a meme for a reason: most people weren't really reviewing code assigned to them. They were effectively rubber-stamping most things.
So now, in the age of AI, those same people are (sometimes still) expected to be responsible for what their automated developer friend Claude is doing. It's absolutely unreasonable to think that most people are giving the PR more than a glance, and in many organizations they're explicitly trying to remove humans from the loop.
One day, AI development and code review will be so good that mistakes like this will be extraordinarily rare. For the near-future, though, I anticipate we'll see more of this before we see less.
Yeah I agree and I think code forges as well as AI harnesses are kinda the killer apps of this (relatively short) era.
I think once we figure out how to tighten the loop of user feedback, expert analysis, automatic/static verification and AI generation then technology is going to make another leap.
The first linked PR (#1218) has only one commit co-authored by Copilot and it's not related to the vulnerability, and neither are the other suggestions in the PR. Am I missing something?
I must be too tired because I cannot figure out what happened in that pull request. The PR/source branch was over a year old with none of the commits adding up to the full diff. There is [1], which introduced the vulnerability but didn't remove the environment variables above, then master is merged into it via [2] (but still doesn't show the variables being removed), yet in the full PR diff they're gone.
In any case, I'm pretty sure you misattributed the vulnerability to Copilot because the PR got squash-merged and _all_ of the changes were then attributed to every contributor in that PR, despite Copilot only appearing on one of the commits.
Thanks @vultour and croemer for your proactiveness -- you are correct,I updated the blog to clarify that Copilot was a co-author that checked the merged PR and code change, and identified it as all-clear without noticing the critical vulnerabilities, it's unclear whether the code-change was AI-Assisted
The interesting lesson here isn't really “AI generated insecure code.” We've had insecure code for decades. The bigger issue is that AI makes it much cheaper to introduce changes, while the cost of reviewing those changes hasn't gone down nearly as much.
The bottleneck is moving from code generation to code verification.
> Workflows like jira_close.yml use deprecated atlassian JIRA actions and have a dependency on the gh-actions repo. This is not ideal and unecessarily complex.
And then goes on:
> PR updates jira_close workflow to use direct API calls via curl.
Duplicating the logic into OUR codebase via a hand rolled curl, so we can get rid of “needless abstractions”. Auch. And of course the whole thing embedded into a yaml file.
This code is the typical kaleidoscope sometimes written by junior devs (and LLMs). On review you just kindly ask to be rewritten into a simple program or just close it as the effort doesn’t worth it.
> However, on issues events, github.event.pull_request is always null.
This is extra dumb because even if you thought this condition was correctly testing the user's identity, it shouldn't have "appeared protective" upon even a moment's thought. If it worked correctly, it would obviously just exclude one bot user while allowing all other users, so it wouldn't provide any protection at all.
But more likely, this condition was never intended to be "protective" at all, and it's only being described that way because the writeup is LLM slop.
Why is the original pattern (with the env var in double quotes) not vulnerable? Why can you close the single quotes early but you can't just include double quotes in your title? Is it something to do with the GitHub templating?
No, Snowflake allowing autofixes compromised their Jira. If you tell someone to shoot you in the foot, and they shoot you in the foot, you shot yourself in the foot, just with more steps. If someone else finds the memo that says you've set up foot shooting as a service, and then they trigger that service, you still shot yourself in the foot.
Help me understand. Snowflake configured their Github repo to allow auto fixes by Copilot. It got merged automatically without anyone's review? And introduced essentially script-injection vulnerability through the title field?
If this is the case, I would say Snowflake should shut down its repo and get off Github asap.
No. A Snowflake maintainer opened a PR, Copilot suggested a change (introducing a vulnerability), the maintainer accepted and committed it to their PR, and another Snowflake maintainer approved and merged the PR.
I don't see anything in the article that says that two maintainers, let alone one, reviewed the PR manually and approved it before merging. Where are you getting this information from?
Admitting that there was human review from not one but two maintainers would entirely defeat the purpose of the article, which was to sell you an AI solution to the AI problem and insist that no human in the loop is better. Which, to be fair, in this case might have been better.
It's appalling that computing in general, and unix in particular, seems to have this habit of intermingling payload and overhead.
It's like in-band signalling in the telephone network, where if you whistled the right tones into your call, you could affect the way the network processed said call. Except Ma Bell responded to that system being exploited by designing a comprehensive overhaul of the way signalling was handled, and spent a squadzillion dollars upgrading millions of tons of switching equipment to categorically exclude that entire class of attack from ever being possible.
Software, on the other hand, would need to replace no equipment whatsoever. Existing processors are perfectly capable of running code that handles the length of a string separately from its contents. There are existing languages that do this, they're just.... not used. String escapes and buffer overflows exist, going on decades now, due to nothing more than laziness, inertia, and negligence.
> It's like in-band signalling in the telephone network...
The major LLM providers claim that they are very committed to security and that their products are very dangerous and capable of great harm.
Given that their tools do what Ma Bell's systems did in the mid-1900s, [0] -which the entire software world relearned was a terrible idea by the early 1990s- they are definitely
1) Lying about the extent of their commitment to security
2) Lying about the extent of the harm that their tools are capable of
3) Both
My money is on #3.
Because of the fact that -in the absence of unambiguous laws that require meaningfully-severe punishment- even the most fucknasty and amateur hour security failures nearly always have little to no impact on the company that causes them, the major LLM providers have absolutely done the smart thing by providing commercial tools that have remote code execution vulns that would automatically give them a Critical CVSS score.
To put it another way: "the market" has no idea how to evaluate computer security claims. Because of this, every dollar you spend on proactively fixing security problems is nearly always a dollar wasted... it's better to wait until someone important gets Big Mad before spending the money. Does this make the world worse? Absolutely! Does this make companies selling software and software services much more money? Definitely!
[0] If the major LLM providers did separate unsanitized data from program instructions and ensure that the two are never mixed, things like [1] would not be possible.
Issues will happen AI, or not AI. It's same as "self driving car made an accident"!
I'm not saying blindly trusting auto-fix is not bad. I'm just saying that interpreting singular issue as way to downplay AI-assisted engineering without giving a "denominator" is not honest reporting.
I'm all for using a council of LLMs, I wrote a tool for it https://github.com/joelio/owl - but you still need to read through PRs yourself, at the very least.
It’s clear that they want this to be true so bad that they’re just not going to do it, and will spend a ton of money on quality gates and mitigation strategies instead of just reading some code.
I have been talking to people who want to autoreview and autoapprove "minor" AI prs. For security especially, I think if the models weren't enough to prevent the issues, they aren't enough to judge what is minor.
I think it's obvious too. I'd call out any case of `${{ }}` interpolation in a `run` block, and it's something I watch for in PRs. I also know other people don't watch for this, as I've corrected it about a hundred times. Over the last 10 years my average colleague understands less and less about injection or to watch for it at layer boundaries.
Use zizmor in CI https://github.com/zizmorcore/zizmor
> The workflow had an if: condition that appeared protective:
> if: (github.event_name == 'issues' && github.event.pull_request.user.login != 'whitesource-for-github-com[bot]')
> However, on issues events, github.event.pull_request is always null. So the condition reduces to (null != 'whitesource-for-github-com[bot]'). This is always true, and every GitHub user passes the gate.
Speaking broadly: it's a massive reminder that AI is trained on a veritable mountain of insecure GitHub Actions examples, many of which "fail open" in highly unpredictable ways even if widely used. Actions is almost unique in this regard, with the combination of a difficult-to-audit language and the type of privileged RCE environment that makes attackers salivate.
(I do think that this stems in part from GitHub's often-inscrutable documentation, and a decision to release Actions without a robust security linting solution, leaving that to the community - but I do understand how it's an uphill battle, and we could have ended up with a much less flexible CI/CD system without this having shipped fast.)
(Source: I am zizmor’s maintainer.)
Nullable event payloads silently null-coalescing to '' are a real "spooky action at a distance" kind of issue, because something that works perfectly when running and being QA'd on PRs, can silently fall apart if made to also run on the main/develop branch (which is only really monitored if the "build breaks," and thus a silent failure or skip might easily sneak through).
Our codebase indeed has comments like this, without which we'd be totally lost:
I imagine that's true of others as well!Use `env:` instead and just work with environment variables in your shell script.
Yes, you still need to vet your script. Quoting is a common source of problems. Use shellcheck. Do not call eval/source/python/perl/whatever with untrusted input.
But you removed one layer of problems already by not pasting a value into your shell script code directly.
We all know that people make mistakes. I.e. crash a car from time to time.
We all are sold a view that AI will save humanity, cure all diseases, solve all problems, allow for autonomous driving and many other (lies?).
While making basic mistakes or crashing on trivial crossroads..
Hype is way overblown.
AI does it a lot faster and ignores rules even harder than humans do, but it's not the root problem here.
But yes, there is an interesting change in the past decade, where everything new must be over-hyped.
Perhaps it is attention overload and needing to shout. Perhaps it’s that technological progress has significantly slowed while communication options have exploded (coincidence?).
I look at it a lot like EVs. They’re great, if your use case is inside the specific band. But, that isn’t who they were being marketed to. And now… “pushback” is putting it lightly.
It's a shame Github is buried under their current server issues, because it would be great to get improvements all of this - at least warning/erroring on these sorts of things themselves.
> Workflows like jira_close.yml use deprecated atlassian JIRA actions and have a dependency on the gh-actions repo. This is not ideal and unecessarily complex. PR updates jira_close workflow to use direct API calls via curl. It preserves custom fields used too.
I won't speak to this projects' management and how they prioritize things, but from my own experience, pre-AI, this type of change would have been firmly in the "this is a minor annoyance, put it in the Tech Debt Backlog alongside the 50000 other tickets" and never actually done. The cost of a human investing the time understanding how to fix the problem, doing code changes, testing them, and deploying them is just way too high for what actual value this change brings, which is close to nothing.
Now with AI, it's as simple as firing up an agent and telling them to make a change; as much effort as writing that backlog Jira ticket in the first place.
Similar to the problem open source is having with low-value PRs, companies are going to have to start realizing that code is not free to review or maintain, even when it's generated for ~free, in their internal processes. Just because an agent can fix a minor tech debt annoyance with a few lines of instructions doesn't mean it should.
[0] https://github.com/snowflakedb/snowflake-connector-net/pull/...
I'm interested in how that turns out 6 months later.
In my team, we have plenty of enhancement requests from users. We address those that make obvious sense and are trivial to do but withhold from others, even though the code change itself is likely small. Because we don't know if there is more than a single user that can actually benefit from it, if it has unintended consequences, or if it causes maintainence issue down the road.
That really is the real question, isn't it?
For me it has been a mixed bag so far, I've seen some companies use this tech in a slow and deliberate manner to do what they were already doing but a little bit faster. I've also seen hail Mary passes where the whole codebase was turned over to agents to go wild on with an undersized team and little to no QA. Time will tell...
Prediction: programming is going to change massively not only because the cost of creating code will go down, but because people are so tired of this sort of gatekeeping "we know better" from programmers.
demand was suppressed by an access bottleneck, the bottleneck is gone, expect a magnitude of change proportional to how much demand was actually pent up rather than proportional to how much programmers currently think was reasonable to deny.
Explicit things I am not saying: gatekeeping was bad, feature creep is good or desirable, etc. etc. etc.
People have to take 'no' for an answer sometimes, even if they don't accept it. You can't really 'gatekeep' your own product.
The work to go from software to usable software system is vast.
I assume the "gatekeeping" decision to not implement a feature request is coming from someone responsible for the product, not from a developer.
In its quest to make markup "human readable", it has created countless footguns.
I honestly prefer XML at this point.
Take CI for example, it was Jenkins and it ran a csh/bash/zsh whatever script and captured the output. Nice and simple (even if the scripts sometimes got insane).
GitHub actions is nothing like that. Weird home grown extensions to YAML with their own idiosyncrasies and dynamically pulling in plugins from god knows where. You can't just take a workflow and execute it locally like you could with a bash script.
Jenkins still exists and it's no less complicated than Github Actions. The complexity gets hidden in obscure script files, obscure tabbed UI, and remote services for doing things Jenkins itself can't do. Defaulting to system tooling makes it almost impossible to predict what a job will do unless you know exactly how paths and tooling are set up (and what versions they're running).
None of my personal experiences with Jenkins had scripts that ran locally, they all relied on pre-installed software on the server because that was the thing people would do before the great YAMLification. You could copy-paste the Jenkins job, but unless you have Jabberwocky v2018.3 installed in /home/JabberWock/RELEASE, the script will fail.
The entire software development flow has been made incredibly complex by hooking up automations into every nook and cranny.
All of these complications need to be enabled manually, though. If your flow is complicated, you can cut it down to manageable size by doing a few more processes manually.
Luckily, all of the simplication and reproduction steps for Github also apply to Jenkins. Github YAML files are just scripts with different syntax, after all. Just like you can run bash locally, you can run act and reproduce whatever Github trigger you need. From there, you can simplify pipelines, stop curl2bashing "plugins", and so on.
In my experience, containerization resolved this issue. Now you just need to be able to "docker run public.ecr.aws/carroll/jabberwocky:v2018.3 [etc]".
Or it's an obscure bash oneliner hidden in another repository as a package that you can depend on, which is either wrapped by another YAML config or maybe some JS.
I don't think the criticisms of Jenkins compare because Jenkins is really much more than a CI layer, predates the concept of devops and our modern understanding of CI/CD, and it has a lot of complexity to make that work. That was back when you'd have IT teams provisioning your servers for you and shit.
At least XML permitted comments, fhe shift to JSON on everything almost makes me nostalgic.
{ "//":"make sure these are divisible by 8", "width": 640, "height": 480 }
It seems like they were thinking of some json parsers that’ll ignore just straight up anything after // before a new line in JSON (looking at you visual studio). So a .NET dev who has never touched anything but Visual Studio may make comments in the appsettings.json for a .NET project and it made all my command line parsers throw errors. Thanks Microsoft.
Better move as much of that as possible into your own scripts. And your scripts can be portable between forges, and even run locally!
How do you specify actions and workflows without interpolation and logic kind sir?
Every time I've tried in the past, something as simple as making a value a list had some nonsense expectations. I can't wrap my head around how that spec got any traction and wasn't laughed off the face of the earth the first time it was looked at by someone who didn't create it.
What I'm seeing now in industry -- and I think this autofix issue is a precise example of it -- is a natural evolution of the "LGTM!" review that's so prevalent in software development and similar disciplines.
For years, the dramatic majority of "code review" was a quick glance followed by "Looks good to me." Sure, critical workflows have more scrutiny. Sure, not everyone fell victim to this trap. Sure, there are many exceptions. But it's a meme for a reason: most people weren't really reviewing code assigned to them. They were effectively rubber-stamping most things.
So now, in the age of AI, those same people are (sometimes still) expected to be responsible for what their automated developer friend Claude is doing. It's absolutely unreasonable to think that most people are giving the PR more than a glance, and in many organizations they're explicitly trying to remove humans from the loop.
One day, AI development and code review will be so good that mistakes like this will be extraordinarily rare. For the near-future, though, I anticipate we'll see more of this before we see less.
I think once we figure out how to tighten the loop of user feedback, expert analysis, automatic/static verification and AI generation then technology is going to make another leap.
However, GitHub Advanced Security, a bot, flagged something but not the real issue. So maybe that bot contributed a false sense of security.
Commit: https://github.com/snowflakedb/snowflake-connector-net/pull/...
In any case, I'm pretty sure you misattributed the vulnerability to Copilot because the PR got squash-merged and _all_ of the changes were then attributed to every contributor in that PR, despite Copilot only appearing on one of the commits.
[1] https://github.com/snowflakedb/snowflake-connector-net/commi...
[2] https://github.com/snowflakedb/snowflake-connector-net/commi...
The bottleneck is moving from code generation to code verification.
> Workflows like jira_close.yml use deprecated atlassian JIRA actions and have a dependency on the gh-actions repo. This is not ideal and unecessarily complex.
And then goes on:
> PR updates jira_close workflow to use direct API calls via curl.
Duplicating the logic into OUR codebase via a hand rolled curl, so we can get rid of “needless abstractions”. Auch. And of course the whole thing embedded into a yaml file.
This code is the typical kaleidoscope sometimes written by junior devs (and LLMs). On review you just kindly ask to be rewritten into a simple program or just close it as the effort doesn’t worth it.
> if: (github.event_name == 'issues' && github.event.pull_request.user.login != 'whitesource-for-github-com[bot]')
> However, on issues events, github.event.pull_request is always null.
This is extra dumb because even if you thought this condition was correctly testing the user's identity, it shouldn't have "appeared protective" upon even a moment's thought. If it worked correctly, it would obviously just exclude one bot user while allowing all other users, so it wouldn't provide any protection at all.
But more likely, this condition was never intended to be "protective" at all, and it's only being described that way because the writeup is LLM slop.
For something as critical as Actions, it’s crazy to me that they wouldn’t fail-closed, and instead fail open when encountering a null. Scary stuff!
If this is the case, I would say Snowflake should shut down its repo and get off Github asap.
It's a very strange thing indeed, but not unexpected: we warned that skills not used will eventually atrophy.
Quote injection still alive and well in 2026. Gawd.
It's like in-band signalling in the telephone network, where if you whistled the right tones into your call, you could affect the way the network processed said call. Except Ma Bell responded to that system being exploited by designing a comprehensive overhaul of the way signalling was handled, and spent a squadzillion dollars upgrading millions of tons of switching equipment to categorically exclude that entire class of attack from ever being possible.
Software, on the other hand, would need to replace no equipment whatsoever. Existing processors are perfectly capable of running code that handles the length of a string separately from its contents. There are existing languages that do this, they're just.... not used. String escapes and buffer overflows exist, going on decades now, due to nothing more than laziness, inertia, and negligence.
The major LLM providers claim that they are very committed to security and that their products are very dangerous and capable of great harm.
Given that their tools do what Ma Bell's systems did in the mid-1900s, [0] -which the entire software world relearned was a terrible idea by the early 1990s- they are definitely
1) Lying about the extent of their commitment to security
2) Lying about the extent of the harm that their tools are capable of
3) Both
My money is on #3.
Because of the fact that -in the absence of unambiguous laws that require meaningfully-severe punishment- even the most fucknasty and amateur hour security failures nearly always have little to no impact on the company that causes them, the major LLM providers have absolutely done the smart thing by providing commercial tools that have remote code execution vulns that would automatically give them a Critical CVSS score.
To put it another way: "the market" has no idea how to evaluate computer security claims. Because of this, every dollar you spend on proactively fixing security problems is nearly always a dollar wasted... it's better to wait until someone important gets Big Mad before spending the money. Does this make the world worse? Absolutely! Does this make companies selling software and software services much more money? Definitely!
[0] If the major LLM providers did separate unsanitized data from program instructions and ensure that the two are never mixed, things like [1] would not be possible.
[1] <https://www.schneier.com/blog/archives/2026/08/prompt-inject...>
Human responsibility over AI oversight folks.. even forgoing AI, we're still gonna get compromised code either way.. deal with it.
I'm not saying blindly trusting auto-fix is not bad. I'm just saying that interpreting singular issue as way to downplay AI-assisted engineering without giving a "denominator" is not honest reporting.
Humans need to review this stuff yall there's no way around that, apparently to some, very inconvenient reality.
Absolutely.
Nothing in the PR jumps out as a red flag. Unless you know how the internals work, I suppose.
Made by AI?